Summary of Introduction to Large Language Models
Large Language Models (LLMs) are a subset of deep learning that can be pre-trained and fine-tuned for specific purposes.
LLMs are trained for general purposes and can be tailored to solve specific problems in different fields using relatively small datasets.
LLMs have three major features: large size in terms of training data and parameters, general purpose to solve common problems, and pre-trained and fine-tuned for specific aims.
Benefits of LLMs include versatility in solving different tasks, minimal field training data requirement, and continuous performance improvement with more data and parameters.
LLM development using pre-trained models requires prompt design, which is creating clear and informative prompts for the model to generate responses.
Different types of LLMs include generic language models, instruction-tuned models, and dialogue-tuned models, each requiring different types of prompts.
Task-specific tuning of LLMs can be done by fine-tuning the model with new data or using parameter-efficient tuning methods to customize the model response.
Google Cloud offers tools like Generative AI Studio, Vertex AI, and Pom APR to explore and customize generative AI models, build generative AI applications, and test Google's Large Language Models and J tools for prototyping.
Speakers/sources: M (custom engineer at Google Cloud)
Notable Quotes
— 11:16 — « hows it going Im M today Im going to »
— 11:22 — « directly however by the time the second »
— 11:48 — « task specific Foundation models »
— 12:26 — « tuned specifically for the legal or medical »
— 13:49 — « models to production and a community »
Category
Educational